Data Analysis
Analyze data skewness to measure the asymmetry of probability distribution and identify data patterns
Call this tool from your code in three languages.
curl -X POST 'https://api.elysiatools.com/en/api/tools/skewness-analyzer' \
-H 'Content-Type: application/json' \
-d '{"dataInput":"Enter your data values separated by commas or new lines...\n\nExamples:\n- Single column: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10\n- Multiple columns: 1, 5, 3, 8, 2, 9, 4, 7, 6, 10\n- One value per line: 1\n2\n3\n4\n5","dataFormat":"single","analysisType":"comprehensive","detectOutliers":true}'Send a POST request with your inputs as JSON. File parameters require a separate upload first.
POST https://api.elysiatools.com/en/api/tools/skewness-analyzer| Name | Type | Required | Description |
|---|---|---|---|
| dataInput | textarea | Yes | — |
| dataFormat | select | Yes | — |
| analysisType | select | Yes | — |
| detectOutliers | checkbox | No | Identify and analyze outliers in the dataset using IQR method |
Text result
{
"result": "Processed text content",
"error": "Error message (optional)",
"message": "Notification message (optional)",
"metadata": {
"key": "value"
}
}Add this tool to your Model Context Protocol server so AI agents can list and call it.
Add this block to your MCP client configuration:
{
"mcpServers": {
"elysiatools-skewness-analyzer": {
"name": "skewness-analyzer",
"description": "Analyze data skewness to measure the asymmetry of probability distribution and identify data patterns",
"baseUrl": "https://api.elysiatools.com/mcp/sse?toolId=skewness-analyzer",
"command": "",
"args": [],
"env": {},
"isActive": true,
"type": "sse"
}
}
}After connecting to the SSE endpoint, list the exposed tools:
{
"jsonrpc": "2.0",
"id": 1,
"method": "tools/list"
}Invoke the tool by its id, passing arguments built from its parameters:
{
"jsonrpc": "2.0",
"id": 2,
"method": "tools/call",
"params": {
"name": "skewness-analyzer",
"arguments": {
"dataInput": "Enter your data values separated by commas or new lines...\n\nExamples:\n- Single column: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10\n- Multiple columns: 1, 5, 3, 8, 2, 9, 4, 7, 6, 10\n- One value per line: 1\n2\n3\n4\n5",
"dataFormat": "single",
"analysisType": "comprehensive",
"detectOutliers": true
}
}
}Questions or issues? Contact [email protected]